Abstract
Background
Atherosclerosis is an inflammatory disease. Many studies demonstrated that hyperglycemia is not only increased inflammatory response, but also is a cause of atherosclerosis, implying that glucose metabolic status may be an important stratification factor when analyzing the relationship between inflammatory levels and subclinical carotid atherosclerosis. The aim of the present study is to assess the relationship between inflammatory levels and subclinical carotid atherosclerosis, stratified by different glucose metabolic status in a general population.
Methods
An assessment was performed in 7975 participants living in Tianjin, China. In the present study, we examined subclinical carotid atherosclerosis, as defined by increased carotid intima‐media thickness [IMT] and plaques. Measurements were performed using a carotid artery B‐mode ultrasound system. The glucose metabolic status was defined by the criteria of the American Diabetes Association, and high‐sensitivity C‐reactive protein (hs‐CRP) as an inflammatory indicator, was measured by immunoturbidimetric assay. Multiple logistic models were used to assess a stratified relationship between hs‐CRP levels and subclinical carotid atherosclerosis. Strata were defined according to glucose metabolic status.
Results
The prevalence of increased IMT and plaques were 27.3% and 21.3%, respectively. The adjusted odds ratios (95% confidence interval) for IMT across hs‐CRP quartiles were as follows: 1.00 (reference), 1.10(0.88‐1.38), 1.08(0.86‐1.35) and 1.32(1.06‐1.66) in blood glucose‐normal subjects; 1.00 (reference), 1.33(0.92‐1.91), 1.33(0.93‐1.91), and 1.59(1.10‐2.30) in prediabetic subjects; 1.00 (reference), 0.94(0.54‐1.62), 1.17(0.65‐2.12) and 0.98(0.55‐1.76) in diabetic subjects, respectively. Similar results were observed for plaques.
Conclusions
Our results suggest that inflammatory levels are differently related to subclinical carotid atherosclerosis by the different glucose metabolic status.
Keywords: glucose metabolic status, high sensitivity C‐reactive protein, subclinical carotid atherosclerosis
1. INTRODUCTION
Cardiovascular diseases (CVDs) are the number one cause of death globally.1 An estimated 17.5 million people died from CVDs in 2012, representing 31% of all global deaths,1 and it has reached epidemic proportions (about 40% of all deaths) of in China.2 The growing burden of CVDs demonstrates an immediate need to clarify the underlying disease mechanisms. This improved understanding will enhance disease prevention. Many evidences demonstrated that atherosclerosis is the main pathological basis of CVDs.3, 4 Atherosclerosis is an inflammatory disease.5 At the same time, many studies have shown that hyperglycemia may itself accelerate atherosclerosis. Hyperglycemia induces glycosylated proteins that interact with a specific receptor (a member of the immunoglobulin superfamily of receptors) present on all cells relevant to the atherosclerotic process. These cells include monocyte‐derived macrophages, endothelial cells, and smooth muscle cells.6, 7 It has also been proposed that hyperglycemia accelerates inflammatory processes through the formation of advanced glycosylation end products (AGEs).8 These studies suggest that glucose metabolic status may be an important stratification factor when analyzing the relationship between inflammatory levels and subclinical carotid atherosclerosis. Detectable but still relatively low levels of high‐sensitivity C‐reactive protein (hs‐CRP), which is known to reflect chronic low‐grade inflammation, has been found to be associated with atherosclerosis and CVDs.9, 10, 11 Many publications have demonstrated that, in addition to its association with subclinical carotid atherosclerosis (as defined by increased carotid intima‐media thickness [IMT] and plaques),12, 13 hs‐CRP also has a strong association with type 2 diabetes mellitus (T2DM).14 Thus, it is conceivable that glucose metabolic status may be a crucial stratification factor for the relationship between subclinical carotid atherosclerosis and hs‐CRP. However, to date, few studies have assessed the relationship between subclinical carotid atherosclerosis and hs‐CRP stratified by glucose metabolic status. Hence, the aim of our study was to investigate how hs‐CRP relates to subclinical carotid atherosclerosis among members of the general adult population with different glucose metabolic statuses.
2. METHODS
2.1. Study population
The study sample of this cross‐sectional study was taken from participants in the Tianjin Chronic Low‐grade Systemic Inflammation and Health (TCLSIH) Cohort, details of which have been published elsewhere.15 This study conformed to the ethical guidelines of the 1975 Declaration of Helsinki. The protocols and procedures of the study were approved by the Institutional Review Board of Tianjin Medical University, and written informed consent was obtained from each participant.
A total of 8949 subjects participated in this study. Some subjects were excluded because of incomplete laboratory data (n = 1) or a history of CVDs (n = 742) or cancer (n = 161) or lack the data of plaque area (n = 70). After excluding those subjects, the final cross‐sectional analysis population comprised 7975 participants including 5988 subjects with normal blood glucose metabolic status, 1434 subjects with prediabetes and 553 subjects with T2DM.
2.2. Assessment of glucose metabolic status
Fasting blood glucose (FBG) and HbA1c were measured by standard methods.15 To measure 2‐hour serum glucose, subjects were given a standard 75‐g glucose solution, and serum glucose was measured at 2 hours after administration during the oral glucose tolerance test. Diabetes can be classified into four clinical categories: type 1 diabetes, type 2 diabetes, other specific types of diabetes because of other causes and gestational diabetes mellitus.16 In undiagnosed participants, T2DM was defined as an FBG ≥126 mg/dL (7.0 mmol/L), or oral glucose tolerance test (OGTT) ≥200 mg/dL (11.1 mmol/L), or HbA1c ≥48 mmol/mol (6.5%) or a history of T2DM based on the American Diabetes Association 2014 criteria.16 Prediabetes was defined as having impaired fasting glucose levels [IFG; 110‐126 mg/dL (6.1‐7.0 mmol/L)] or impaired glucose tolerance [IGT; 2‐hour values during OGTT of 140‐200 mg/dL (7.8‐11.1 mmol/L)].17 In the subjects with normal glucose metabolic status, exclusion criteria were diabetes and prediabetes.
2.3. Assessment of hs‐CRP
Levels of serum hs‐CRP were measured by high‐sensitivity immunoturbidimetric assay using a Hitachi 917 analyzer (Roche Diagnostics, Mannheim, Germany), and expressed as mg/L. The detection limit for hs‐CRP was 0.01 mg/L. To investigate how the hs‐CRP levels were related to the prevalence and incidence of increased IMT or plaques, we divided participants into four categories according to observed concentrations of hs‐CRP.
2.4. Assessment of IMT, plaques, and plaque area
One trained sonographer performed the carotid ultrasonography using echo color‐doppler ultrasonography (iU Elite, Royal Philips of the Netherlands, America) equipped with linear probe, model L9‐3, measure the carotid intima‐media thickness (IMT) and plaques. All participants were asked to remain in the supine position with the head extended and turned 45° to the contralateral side of the artery during the examination. Measurements were made of common carotid artery (CCA) and carotid sinus after the examination of a longitudinal section of 10 mm at a distance of 1 cm from the bifurcation. Carotid IMT was evaluated as the distance between the lumen‐intima interface and the media‐adventitia interface. Intima and media thicknesses were measured as the distance from the main edge of the first to the main edge of the second echogenic line. Increased IMT was characterized by the largest IMT (≥1.0 mm) in the CCA on the left or right sides and the largest IMT (≥1.2 mm) in the carotid sinus on the left or right sides.18 The procedure for detecting plaques involved scanning the near and far walls of the common carotid arteries, the carotid bifurcation, the external carotid artery, and the internal carotid artery. We defined a plaque as a thickness of ≥1.5 mm as measured from the media‐adventitia interface to the intima‐lumen interface. The area of each plaque was calculated as the lesion height (in mm) multiplied by the lesion length (in mm). In those participants with multiple plaques, plaque area was the sum of the areas of all plaques observed.
2.5. Assessment of other variables
Blood pressure (BP) was measured twice from the right arm using an automatic device (Andon, Tianjin, China) after 5 minutes of rest in a seated position. The mean of these two measurements was taken as the BP value. Triglycerides (TG), total cholesterol (TC), low‐density lipoprotein cholesterol (LDL‐C), and high‐density lipoprotein cholesterol (HDL‐C) were measured using by standard methods.15 Fibrinogen was measured according to Clauss using STA Fibrinogen (Diagnostica Stago; des Châtaigniers, France; detection level 2 g/L; coefficient of variation ≤5.0%). Anthropometric parameters (height and body weight) were measured by experienced physicians and body mass index (BMI) was calculated as weight in kilograms divided by height in meters' square (kg/m2). Waist circumference (WC) was measured at the umbilical level with participants standing and breathing normally. Metabolic syndrome (MetS) was defined on the basis of the criteria of the American Heart Association scientific statements of 2009.19 Participants were considered to have MetS when they presented three or more of the following components: (a) elevated WC for Chinese individuals (≥85 cm in males; ≥80 cm in females), (b) elevated TG (≥1.7 mmol/L), or drug treatment for elevated HDL, (c) reduced HDL (<1.0 mmol/L in males; <1.3 mmol/L in females) or drug treatment for reduced HDL, (d) elevated BP (systolic BP ≥130 mm Hg and/or diastolic BP ≥85 mm Hg) or antihypertensive drug treatment, (e) elevated fasting glucose (≥5.56 mmol/L) or drug treatment for elevated glucose. Information on sex, age, alcohol, and tobacco habits were obtained from a standardized questionnaire. A detailed personal and family history of physical illness and current medications was noted from “yes” or “no” responses to relevant questions.
2.6. Statistical analysis
All statistical analyses were performed using the Statistical Analysis System 9.3 edition for Windows (SAS Institute, Inc., Cary, NC, North Carolina). Because all observed continuous variables were not normally distributed, we normalized these variables using logarithmic transformation before analysis. Adjusted continuous variables are shown as the geometric means (95% confidence interval [CI]), categorical variables are presented as percentages. For baseline characteristics analysis, continuous variables and proportional variables were compared with analysis of variance (anova) and logistic regression analysis, respectively. For further analysis, the appearances of increased carotid IMT and plaques were used as dependent variables, and the quartiles of hs‐CRP levels were used as independent variables. The multiple logistic regression analysis was performed to explore the relationship of serum hs‐CRP levels and subclinical carotid atherosclerosis. To assess the relationship between elevated serum hs‐CRP and subclinical carotid atherosclerosis, an unadjusted logistic regression model, an age, sex, BMI‐adjusted model, and a fully‐adjusted model, including age, sex, BMI, smoking status, drinking status, hypertension, hyperlipidemia, MetSs, and family history of cardiovascular diseases, hypertension, and diabetes were used. The analysis of covariance (ancova) was used to analyze the relationship of serum hs‐CRP levels and plaque area. The differences of plaque area among quartiles of hs‐CRP levels were tested by the Bonferroni post‐hoc test. Odds ratios (ORs) (95% CI) and geometric mean (95% CI) were calculated. All P‐values for linear trends were calculated using the median value of quartiles of hs‐CRP. All tests were two‐tailed and P < 0.05 was defined as statistically significant.
3. RESULTS
In the cross‐sectional analysis, 5988 of the 7975 participants (75.1%) were at normal glucose metabolic status, 1434 of the 7975 participants (18.0%) were prediabetes, 553 of the 7975 participants (6.93%) were T2DM. Overall mean ± SD age was 52.1 ± 10.5 years. The overall prevalence of increased IMT and plaques were 27.3% (2180 of 7975) and 21.3% (1700 of 7975), respectively. The characteristics of participants across blood glucose metabolic status for cross‐sectional analysis are presented in Table 1. Compared to participants free of diabetes, those with prediabetes or T2DM tended to be older, to have higher proportion of males, to have higher BMI, WC, and levels of TC, FBG, LDL‐C, fibrinogen, and hs‐CRP, to have bigger plaque area, and to have a higher SBP, DBP, but a lower level of HDL‐C (all P=value < 0.0001); in addition, a higher proportion of these participants were current smokers and drinkers and had a family history of diabetes; however, a lower proportion of these participants had a family history of CVDs and hypertension (P‐value < 0.05). Other than these results, no significant differences were observed between the participants with different blood glucose metabolic statuses.
Table 1.
Participant characteristics by categories of blood glucose metabolic status
| Categories of blood glucose metabolic status | P‐valuea | |||
|---|---|---|---|---|
| Normal | Prediabetes | T2DM | ||
| No. of subjects | 5988 | 1434 | 553 | — |
| Age (years) | 49.8 (49.6, 50.1)b | 54.5 (53.9, 55.0) | 55.8 (54.9, 56.8) | <0.0001 |
| Sex (males, %) | 54.1 | 67.0 | 73.2 | <0.0001 |
| BMI (kg/m2) | 24.8 (24.7, 24.8) | 26.5 (26.4, 26.7) | 27.0 (26.7, 27.3) | <0.0001 |
| WC (cm) | 84.9 (84.7, 85.2) | 90.9 (90.4, 91.5) | 93.4 (92.5, 94.3) | <0.0001 |
| TC (mmol/L) | 4.97 (4.95, 4.99) | 5.17 (5.12, 5.22) | 5.17 (5.09, 5.25) | <0.0001 |
| LDL‐C (mmol/L) | 2.89 (2.87, 2.91) | 3.06 (3.01, 3.11) | 3.00 (2.93, 3.08) | <0.0001 |
| HDL‐C (mmol/L) | 1.32 (1.32, 1.33) | 1.22 (1.21, 1.24) | 1.14 (1.12, 1.17) | <0.0001 |
| SBP (mmol/L) | 121.7 (121.3, 122.2) | 131.3 (130.4, 132.2) | 135.4 (133.8, 136.9) | <0.0001 |
| DBP (mmol/L) | 77.5 (77.2, 77.7) | 82.8 (82.2, 83.4) | 84.9 (83.9, 85.9) | <0.0001 |
| FBG (mmol/L) | 4.85 (4.84, 4.87) | 5.99 (5.96, 6.02) | 8.58 (8.51, 8.65) | <0.0001 |
| Hs‐CRP (mg/L) | 0.81 (0.79, 0.83) | 1.16 (1.10, 1.22) | 1.27 (1.17, 1.38) | <0.0001 |
| Fibrinogen (g/L) | 2.84 (2.83, 2.85) | 2.93 (2.91, 2.96) | 2.95 (2.90, 2.99) | <0.0001 |
| Plaque area (mm2) | 1.84 (1.78, 1.90) | 2.70 (2.52, 2.90) | 4.18 (3.73, 4.68) | <0.0001 |
| MetS (%) | 24.5 | 77.5 | 86.6 | <0.0001 |
| Carotid plaques (%) | 17.8 | 28.9 | 40.1 | <0.0001 |
| Increased carotid IMT (%) | 22.9 | 36.5 | 51.7 | <0.0001 |
| Smoking status (%) | ||||
| Smoker | 27.1 | 30.2 | 33.8 | <0.001 |
| Ex‐smoker | 5.53 | 8.13 | 8.87 | <0.0001 |
| Non‐smoker | 67.3 | 61.7 | 57.4 | <0.0001 |
| Drinker (%) | ||||
| Everyday | 5.22 | 8.87 | 7.58 | <0.001 |
| Sometime | 53.2 | 55.9 | 56.7 | 0.06 |
| Ex‐drinker | 5.50 | 4.04 | 6.18 | 0.52 |
| Nondrinker | 36.1 | 31.2 | 29.5 | <0.001 |
| Family history of diseases (%) | ||||
| CVDs | 33.0 | 31.0 | 28.8 | 0.02 |
| Hypertension | 51.1 | 47.1 | 44.1 | <0.001 |
| Hyperlipidemia | 0.18 | 0.00 | 0.00 | — |
| Diabetes | 21.0 | 27.0 | 39.8 | <0.0001 |
Abbreviations: BMI, body mass index; CVDs, cardiovascular diseases; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL‐C, high density lipoprotein‐cholesterol; hs‐CRP, high‐sensitivity C‐reactive protein; IMT, intima‐media thickness; LDL‐C, low density lipoprotein cholesterol; MetS, metabolic syndrome; TC, total cholesterol; TG, triglycerides; T2DM, type 2 diabetes mellitus; WC, waist circumference; SBP, systolic blood pressure.
Analysis of variance or logistic regression analysis.
Geometric mean (95% confidence interval) (all such values).
In this analysis, we divided hs‐CRP levels into four categories according to observed concentrations of hs‐CRP of participants with different blood glucose metabolic statuses. Table 2 shows the crude and adjusted relationship between quartiles of hs‐CRP and IMT in participants with different blood glucose metabolic statuses. After adjusting for potential confounders, in the final multivariate models, the ORs (95% CI) for IMT across hs‐CRP quartiles were as follows: 1.00 (reference), 1.10 (0.88‐1.38), 1.08 (0.86‐1.35), and 1.32 (1.06‐1.66) in blood glucose‐normal subjects (P for trend = 0.011); 1.00 (reference), 1.33 (0.92‐1.91), 1.33 (0.93‐1.91) and 1.59 (1.10‐2.30) in prediabetic subjects (P for trend = 0.04); 1.00 (reference), 0.94 (0.54‐1.62), 1.17 (0.65‐2.12) and 0.98 (0.55‐1.76) in T2DM subjects (P for trend = 0.97), respectively. Table 3 shows the crude and adjusted relationship between quartiles of hs‐CRP and plaques in participants with different blood glucose metabolic statuses. After adjusting for potential confounders, in the final multivariate models, the ORs (95% CI) for plaques across hs‐CRP quartiles were as follows: 1.00 (reference), 1.21 (0.96‐1.52), 1.31 (1.05‐1.64), and 1.39 (1.11‐1.75) in blood glucose‐normal subjects (P for trend = 0.014); 1.00 (reference), 1.03 (0.70‐1.50), 1.32 (0.92‐1.91) and 1.76 (1.21‐2.56) in prediabetic subjects (P for trend <0.001); 1.00 (reference), 0.96 (0.57‐1.63), 0.78 (0.44‐1.37) and 1.47 (0.85‐2.57) in T2DM subjects (P for trend = 0.09), respectively. The adjusted relationships between quartiles of hs‐CRP and plaque area were shown in Table 3. The plaque area in the lowest and the second lowest quartiles was significantly different from the plaque area in the highest quartile of serum hs‐CRP concentration (Bonferroni‐corrected P‐value < 0.05) in blood glucose‐normal and prediabetic subjects. Similar results were not observed in T2DM subjects.
Table 2.
Adjusted relationship of quartiles of serum hs‐CRP concentration and prevalence of increased carotid IMT in participants with different glucose metabolic statuses
| Quartiles of serum hs‐CRP concentration | P for trenda | ||||
|---|---|---|---|---|---|
| Level 1 | Level 2 | Level 3 | Level 4 | ||
| Normal (n = 5988) | |||||
| Serum hs‐CRP concentration (mg/L, range) | 0.01‐0.40 | 0.41‐0.74 | 0.75‐1.44 | ≥1.45 | — |
| No. of subjects | 1527 | 1468 | 1493 | 1500 | — |
| No. of increased carotid IMT | 225 (14.73%) | 322 (21.93%) | 369 (24.72%) | 455 (30.33%) | — |
| Crude | 1.00 (reference) | 1.63 (1.35, 1.96)b | 1.90 (1.58, 2.29) | 2.52 (2.11, 3.02) | <0.0001 |
| Model 1c | 1.00 (reference) | 1.14 (0.91, 1.42) | 1.16 (0.93, 1.45) | 1.43 (1.15, 1.79) | <0.01 |
| Model 2d | 1.00 (reference) | 1.10 (0.88, 1.38) | 1.08 (0.86, 1.35) | 1.32 (1.06, 1.66) | 0.011 |
| Prediabetes (n = 1434) | |||||
| Serum hs‐CRP concentration (mg/L, range) | 0.10‐0.60 | 0.61‐1.06 | 1.07‐2.07 | ≥2.09 | — |
| No. of subjects | 392 | 326 | 357 | 359 | — |
| No. of increased carotid IMT | 115 (29.34%) | 119 (36.50%) | 136 (38.10%) | 153 (42.62%) | — |
| Crude | 1.00 (reference) | 1.39 (1.01, 1.90) | 1.48 (1.09, 2.01) | 1.79 (1.32, 2.42) | <0.001 |
| Model 1c | 1.00 (reference) | 1.36 (0.95, 1.95) | 1.39 (0.97, 1.98) | 1.68 (1.17, 2.41) | 0.014 |
| Model 2d | 1.00 (reference) | 1.33 (0.92, 1.91) | 1.33 (0.93, 1.91) | 1.59 (1.10, 2.30) | 0.04 |
| T2DM (n = 553) | |||||
| Serum hs‐CRP concentration (mg/L, range) | 0.10‐0.65 | 0.68‐1.20 | 1.23‐2.30 | ≥2.32 | — |
| No. of subjects | 139 | 148 | 127 | 139 | — |
| No. of increased carotid IMT | 68 (48.92%) | 76 (51.35%) | 69 (54.33%) | 73 (52.52%) | — |
| Crude | 1.00 (reference) | 1.10 (0.69, 1.75) | 1.24 (0.77, 2.02) | 1.16 (0.72, 1.85) | 0.59 |
| Model 1c | 1.00 (reference) | 1.14 (0.68, 1.92) | 1.44 (0.83, 2.52) | 1.20 (0.69, 2.06) | 0.59 |
| Model 2d | 1.00 (reference) | 0.94 (0.54, 1.62) | 1.17 (0.65, 2.12) | 0.98 (0.55, 1.76) | 0.97 |
Abbreviations: hs‐CRP, high‐sensitivity C‐reactive protein; IMT, intima‐media thickness; T2DM, type 2 diabetes mellitus.
Multiple logistic regression analysis.
Adjusted odds ratios (95% confidence interval) (all such values).
Adjusted for age, sex, body mass index.
Adjusted for age, sex, body mass index, smoking status, drinking status, hypertension, hyperlipidemia, metabolic syndrome, and family history of cardiovascular diseases, hypertension, and diabetes.
Table 3.
Adjusted relationship of quartiles of serum hs‐CRP concentration and prevalence of carotid plaques and plaque area in participants with different glucose metabolic statuses
| Quartiles of serum hs‐CRP concentration | P for trenda | P‐valuea | ||||
|---|---|---|---|---|---|---|
| Level 1 | Level 2 | Level 3 | Level 4 | |||
| Normal (n = 5988) | ||||||
| Serum hs‐CRP concentration (mg/L, range) | 0.01–0.40 | 0.41‐0.74 | 0.75‐1.44 | ≥1.45 | — | — |
| No. of subjects | 1527 | 1468 | 1493 | 1500 | — | — |
| No. of carotid plaques | 180 (11.79%) | 252 (17.17%) | 300 (20.09%) | 332 (22.13%) | — | — |
| Crude | 1.00 (reference) | 1.55 (1.26, 1.91)† | 1.88 (1.54, 2.30) | 2.13 (1.75, 2.60) | <0.0001 | — |
| Model 1b | 1.00 (reference) | 1.23 (0.99, 1.54) | 1.38 (1.11, 1.72) | 1.47 (1.18, 1.84) | <0.01 | — |
| Model 2c | 1.00 (reference) | 1.21 (0.96, 1.52) | 1.31 (1.05, 1.64) | 1.39 (1.11, 1.75) | 0.014 | — |
| Plaque area (mm2)c | 2.14 (1.47, 3.12)d , e | 2.24 (1.53, 3.26)b | 2.30 (1.58, 3.36) | 2.54 (1.74, 3.71) | 0.10 | <0.01 |
| Prediabetes (n = 1434) | ||||||
| Serum hs‐CRP concentration (mg/L, range) | 0.10–0.60 | 0.61–1.06 | 1.07‐2.07 | ≥2.09 | — | — |
| No. of subjects | 392 | 326 | 357 | 359 | — | — |
| No. of carotid plaques | 94 (23.98%) | 82 (25.15%) | 109 (30.53%) | 129 (35.93%) | — | — |
| Crude | 1.00 (reference) | 1.07 (0.76, 1.50) | 1.39 (1.01, 1.93) | 1.78 (1.30, 2.45) | <0.0001 | — |
| Model 1‡ | 1.00 (reference) | 1.07 (0.74, 1.55) | 1.44 (1.01, 2.07) | 1.92 (1.34, 2.77) | <0.001 | — |
| Model 2d | 1.00 (reference) | 1.03 (0.70, 1.50) | 1.32 (0.92, 1.91) | 1.76 (1.21, 2.56) | <0.001 | — |
| Plaque area (mm2)c | 2.82 (1.89, 4.21)f | 2.86 (1.91, 4.29)f | 3.46 (2.31, 5.19) | 3.90 (2.61, 5.84) | 0.06 | <0.01 |
| T2DM (n = 553) | ||||||
| Serum hs‐CRP concentration (mg/L, range) | 0.10–0.65 | 0.68‐1.20 | 1.23‐2.30 | ≥2.32 | — | — |
| No. of subjects | 139 | 148 | 127 | 139 | — | — |
| No. of carotid plaques | 53 (38.13%) | 58 (39.19%) | 43 (33.86%) | 68 (48.92%) | — | — |
| Crude | 1.00 (reference) | 1.05 (0.65, 1.68) | 0.83 (0.50, 1.37) | 1.55 (0.97, 2.51) | 0.051 | — |
| Model 1b | 1.00 (reference) | 1.05 (0.64, 1.74) | 0.87 (0.51, 1.49) | 1.70 (1.01, 2.88) | 0.03 | — |
| Model 2c | 1.00 (reference) | 0.96 (0.57, 1.63) | 0.78 (0.44, 1.37) | 1.47 (0.85, 2.57) | 0.09 | — |
| Plaque area (mm2)c | 1.81 (0.85, 3.83) | 1.71 (0.80, 3.65) | 1.63 (0.75, 3.51) | 2.53 (1.19, 5.39) | 0.61 | 0.11 |
Abbreviations: hs‐CRP, high‐sensitivity C‐reactive protein; T2DM, type 2 diabetes mellitus.
Analysis of covariance or multiple logistic regression analysis.
Adjusted odds ratios (95% confidence interval) (all such values).
Adjusted for age, sex, body mass index.
Adjusted for age, sex, body mass index, smoking status, drinking status, hypertension, hyperlipidemia, metabolic syndrome, and family history of cardiovascular diseases, hypertension, and diabetes.
Adjusted geometric mean (95% confidence interval) (all such values).
P < 0.001 as compared with level 4 of serum hs‐CRP concentration.
P < 0.05 as compared with level 4 of serum hs‐CRP concentration.
4. DISCUSSION
The present study first evaluated the relationship between hs‐CRP and subclinical carotid atherosclerosis stratified by the different glucose metabolic status in a general population. The results showed that the relationship between hs‐CRP and subclinical carotid atherosclerosis was stronger in adults with prediabetes than with normal glucose metabolic status, but similar results were not observed in patients with T2DM.
Because age, sex, and BMI have been shown to be related to hs‐CRP levels and to subclinical carotid atherosclerosis,20, 21 we first adjusted for these variables in model 1. Adjustment for these variables only slightly attenuated the relationship we observed between hs‐CRP and subclinical carotid atherosclerosis. This indicates that the effects of these variables on the relationship are weak. Next, we adjusted for smoking status, drinking status, hypertension, hyperlipidemia, and MetS (factors that influence both hs‐CRP levels and atherosclerosis),22, 23, 24 as well as for genetic factors.25 These genetic factors include family history of CVDs, hypertension, and diabetes, all of which have been shown to have an association with CVDs.26, 27 The results of model 2, in which we adjusted for all variables, did not change significantly compared with model 1.
The hypothesis of this study is that glucose metabolic status maybe a crucial stratification factor for the relationship between hs‐CRP and subclinical carotid atherosclerosis. So far, there have been conflicting findings regarding the relationship between hs‐CRP and subclinical carotid atherosclerosis in diabetic patients. Three cross‐sectional studies and a cohort study reported that hs‐CRP was related to IMT in patients with diabetes.28, 29, 30, 31 In contrast, a cross‐sectional study showed that CRP was not correlated of carotid artery IMT in men with diabetes.32 On the other hand, to date, two cross‐sectional studies found that hs‐CRP levels were significantly correlated with the IMT of nondiabetic patients,28, 29 but a cohort study reported that hs‐CRP was no longer independently associated with IMT progression in the nondiabetic subjects.30 Because prediabetes is an important precursor of T2DM, our research analyzed the relationship between hs‐CRP and subclinical carotid atherosclerosis in participants with T2DM, prediabetes, and normal glucose metabolic status. However, contrary to a previous study,30 our study found that hs‐CRP was related to subclinical carotid atherosclerosis in both prediabetic patients and individuals with normal glucose metabolic status. Although the reasons for these discrepancies remain unclear, the differences in confounding factors, study design, stratified method, ethnic differences might partly explain the cause for conflicting results. Further studies are necessary to explore whether these results of the present study can also be observed in other general populations.
The present observation of differences between adults with normal glucose metabolic status and T2DM regarding hs‐CRP and subclinical carotid atherosclerosis may be explained. First, one possible cofounder in our study may result from the fact that T2DM patients are more likely to be using glucose‐lowering therapies, such as metformin, pioglitazone, and empagliflozin.33 Furthermore, one study has demonstrated that, in addition to lowering blood glucose levels, those therapies also play an important role in atherosclerosis.33 Meanwhile, it is known that T2DM is accompanied by some complications, including nephropathy, neuropathy, cardiomyopathy, and so on.34 Another possible confounding effect might have been an obviously larger proportion of patients with complication‐treatment, for example, statins in the T2DM group, which known to affect inflammation, but this was not observed. Second, it has also indicated that our understanding of pathogenesis of CVDs because of diabetes has improved significantly, with hyperinsulinemia, insulin resistance, and hypercoagulability playing a role in the excess CVDs‐risk in patients with diabetes.35, 36 Those above may influence the real relationship between hs‐CRP and subclinical carotid atherosclerosis in T2DM subjects.
Some potential mechanisms by which hs‐CRP may play a role in atherosclerosis in general population have been implicated. First, hs‐CRP deposition induces recruitment of monocytes to the atherosclerotic lesion. Monocyte infiltration into the arterial wall is a two‐step process that involves adherence to the activated endothelium and directed migration to a chemotactic gradient.37 Second, hs‐CRP upregulates angiotensin type 1 receptor (AT1‐R) ‐ mediated atherosclerotic events in vascular smooth muscle in vitro and in vivo.38 Third, hs‐CRP may also promote atherogenesis by inducing endothelial dysfunction.39, 40 Furthermore, in this study, we found that the relationship between hs‐CRP and subclinical carotid atherosclerosis was more marked in subjects with prediabetes than with normal blood glucose metabolic status. Some findings suggested that hyperglycemia not only facilitates inflammatory processes,8 but also induces a large number of alterations that potentially accelerate the atherosclerosis process,6 which pointing to the possibility that hyperglycemia might strengthen the proatherogenic effects of hs‐CRP on subclinical carotid atherosclerosis. Further studies to test this hypothesis in prediabetic subjects are warranted.
Our study has several notable limitations. First, because this is a cross‐sectional study, further prospective studies and randomized controlled clinical trials are needed to identify a causal relationship between hs‐CRP and subclinical carotid atherosclerosis in subjects with different blood glucose metabolic statuses. Second, the results of the present study need to be replicated in larger studies, since the group of patients with T2DM was relatively small in comparison to the other two groups of the study. Third, although a variety of confounders were considered, some unknown residual confounding cannot be excluded. Fourth, this study did not include information about compensated and decompensated diabetes; thus, we cannot analyze the relationship between hs‐CRP and carotid atherosclerosis within each population. Fifth, although the homeostasis model assessment‐insulin resistance index is a valid measure to determine insulin‐resistance, we could not evaluate the impact of this factor in the present study because of lack of information. Finally, our results cannot exclude that hs‐CRP is associated with atherosclerosis in diabetic patients as this subgroup of patients probably already has an elevated hs‐CRP. Therefore, further cohort studies are needed to verify whether baseline hs‐CRP values can predict the progress of carotid atherosclerosis in diabetic patients.
5. CONCLUSIONS
The present study suggests that elevated hs‐CRP levels are more strongly related to the subclinical carotid atherosclerosis in adults with prediabetes than with normal glucose metabolic status. But this relationship has disappeared in patients with T2DM. These results imply that inflammatory levels may contribute to the regulation of subclinical carotid atherosclerosis by the different glucose metabolic status. Further cohort study and clinical trial are needed to verify our results and find more effective strategy to prevent subclinical carotid atherosclerosis by controlling inflammation levels.
CONFLICTS OF INTEREST
The authors have no conflicts of interest to report.
ACKNOWLEDGMENTS
We gratefully acknowledge all the people who have participated in this study, and the Tianjin Medical University General Hospital Health Management Centre for making the study possible. This study was supported by grants from the National Natural Science Foundation of China (No. 81673166, 81 372 118, 81 372 467 and 81 302 422), the Key Technologies R&D Program of Tianjin (Key Project: No. 11ZCGYSY05700, 12ZCZDSY20400, 13ZCZDSY20200, and 15YFYZSY00020), the National Science and Technology Support Program (No. 2012BAI02B02), 2012 and 2016 Chinese Nutrition Society (CNS) Nutrition Research Foundation—DSM Research Fund (No. 2014‐071 and 2016‐046), the Technologies Development Program of Beichen District of Tianjin (No. bcws2013‐21 and bcws2014‐05), the technologies project of Tianjin Binhai New Area (No. February 4, 2013 and February 6, 2013), the Science Foundation of Tianjin Medical University (No. 2010KY28 and 2013KYQ24), the Key Laboratory of Public Health Safety (Fudan University), Ministry of Education (No. GW2014‐5), and the National Training Programs of Innovation and Entrepreneurship for Undergraduates (No. 201510062013), China.
Su H, Pei Y, Tian C, et al. Relationship between high‐sensitivity C‐reactive protein and subclinical carotid atherosclerosis stratified by glucose metabolic status in Chinese adults. Clin Cardiol. 2019;42:39–46. 10.1002/clc.23095
Haiyan Su and Yinghua Pei contributed equally to this study.
Funding information National Natural Science Foundation of China, Grant/Award Numbers: No. 81673166, 81372118, 81372467 and 81302422, 81673166, 81 372 118, 81 372 467 and 81 302 422; the Science Foundation of Tianjin Medical University , Grant/Award Number: 2010KY28 and 2013KYQ24; 2012 and 2016 Chinese Nutrition Society (CNS) Nutrition Research Foundation‐DSM Research Fund , Grant/Award Number: 2014‐071 and 2016‐046; the Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, Grant/Award Number: GW2014‐5; the key technologies R&D program of Tianjin , Grant/Award Number: No. 11ZCGYSY05700, 12ZCZDSY20400, 13ZCZDSY20200, a; the National Science and Technology Support Program , Grant/Award Number: 2012BAI02B02; the National Training Programs of Innovation and Entrepreneurship for Undergraduates, Grant/Award Number: 201510062013; the Technologies development program of Beichen District of Tianjin , Grant/Award Number: bcws2013‐21 and bcws2014‐05; the technologies project of Tianjin Binhai New Area, Grant/Award Number: 2013‐02‐04 and 2013‐02‐06; Tianjin Medical University
Contributor Information
Qing Zhang, Email: niukaijun@tmu.edu.cn.
Kaijun Niu, Email: nkj0809@gmail.com.
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